NDLI: Can Biomethane Potential (BMP) Be Predicted from Other Variables Such As Biochemical Composition in Lignocellulosic Biomass and Related Organic Residues?

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Can Biomethane Potential (BMP) Be Predicted from Other Variables Such As Biochemical Composition in Lignocellulosic Biomass and Related Organic Residues?

Content Provider	Springer Nature Link
Author	Bayard, Rémy Liu, Xun Benbelkacem, Hassen Buffiere, Pierre Gourdon, Rémy
Copyright Year	2015
Abstract	The potential of methane production by anaerobic digestion of lignocellulosic biomass depends not only on the availability of the resources in the considered territory, but also on their physico-chemical characteristics. Relevant methods of characterization are, therefore, needed to select and possibly combine the most appropriate biomass substrates in order to optimize energy recovery through anaerobic digestion processes. The objective of the present study was to determine whether biomethane potential of such substrates could be predicted from a limited number of variables more rapidly or determined more easily. A set of 36 biomass substrates and organic residues from a variety of origins was analyzed for total and easily hydrosoluble organic matter fractions (volatile solid, VS and soluble chemical oxygen demand, SCOD), neutral detergent soluble fraction (SOL), hemicelluloses (HEM), cellulose (CELL), and lignin-like residual fractions (RES). Bioreactivity of all samples was also measured by experimental assays (biochemical oxygen demand, BOD and biochemical methane potential, BMP). The whole set of data thereby obtained was analyzed statistically considering one dependent variable (BMP), and six independent variables (SCOD, SOL, HEM, CELL, RES, and BOD). Partial least square (PLS) analysis revealed very clearly a positive correlation between BMP and BOD, which were both anti-correlated with RES. On the other hand, no correlations were observed between BMP, SCOD, HEM, and CELL contents. PLS analysis showed that BMP was significantly correlated to the six independent variables. The most influential variables were found to be RES and BOD, and a polynomial model was successfully validated for the prediction of BMP from RES and BOD.
Starting Page	610
Ending Page	623
Page Count	14
File Format	PDF
ISSN	19391234
Journal	BioEnergy Research
Volume Number	9
Issue Number	2
e-ISSN	19391242
Language	English
Publisher	Springer US
Publisher Date	2015-12-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Biochemical methane potential Biodegradation Anaerobic digestion Biomass Organic waste Lignocellulose Cellulose Lignin Correlation Statistical analysis Plant Sciences Plant Genetics & Genomics Plant Breeding/Biotechnology Plant Ecology Wood Science & Technology
Content Type	Text
Resource Type	Article
Subject	Renewable Energy, Sustainability and the Environment Agronomy and Crop Science

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